DESIGN AND IMPLEMENTATION OF A GENE NETWORK REVERSE ENGINEERING METHOD BASED ON MUTUAL INFORMATION
Bibliographic record
Abstract
In this paper, the authors describe a gene network reverse engineering method, which employs mutual information to infer the connections between the genes. Given the expression profile of the genes, determined for different conditions, the method calculates the similarity matrix corresponding to the mutual information between each pair of genes. The approximated matrix can be subsequently refined by using a data processing inequality and an analytically estimated threshold for the statistical significance of mutual information. The authors have used the proposed method to reconstruct a network of 2041 gene transcription factors, measured over 79 human tissues. The numerical results show that the connectivity of the transcription factors network is characterized by a scale free distribution, with an exponent of the power law between 1.5 and 2. The power law for the connectivity distribution implies that the network is extremely heterogeneous; i.e., its topology is dominated by a few highly connected genes, which link the rest of the loosely-connected genes to the system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".